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CALVING HABITAT SELECTION OF PARTRUIENT MOOSE (Alces alces) IN NORTHERN MAINE
Brett P. Skelly
A senior thesis submitted in partial fulfillment of the requirements for the degree of
Bachelor of Science
Wildlife and Fisheries Management
At
Unity College
Spring, 2016
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CALVING HABITAT SELECTION OF PARTRUIENT MOOSE (Alces alces) IN NORTHERN MAINE
Brett P. Skelly
A senior thesis completed under the supervision of
Advisor Signature Advisor Signature
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Date Date
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Electronic Thesis Release Form
Student name: Brett Skelly __________________________ _
Student ID: _00000020041 Degree in _Wildlife and Fisheries Management (discipline)
TITLE:_ CALVING HABITAT SELECTION OF PARTRUIENT MOOSE (Alces alces) IN NORTHERN
MAINE~~~~~~~~~~~~~~~~~~~~~~~~
I agree to allow the Dorothy Webb Quimby Library, Unity College to serve as the institutional repository of
my thesis.
I hereby grant to Unity College and its agents the non-exclusive, worldwide right to reproduce, distribute,
display and transmit my thesis or dissertation in such tangible and electronic formats as may be in existence
now or developed in the future. I retain all ownership rights to the copyright of the thesis or dissertation,
including the right to use it in whole or in part in future works.
I certify that the version I have submitted is the same version that was approved by the appropriate college
authority.
I affirm that the content of my submission does not, to the best of my knowledge, infringe upon anyone's
copyright through plagiarism, unapproved reproduction of materials or improper citation.
My signature on this form signi/i~s !JJ9~Jpcknowledge and agree to these terms.
Student sig~ature ~"ti: ~~
Date 1.3 4/¢y ;?o/6
Please provide a copy of your thesis along with a signed hard copy of this form to:
Dorothy Webb Quimby Library Unity College 90 Quaker Hill Road Unity, ME 04988 207.948.9178 librarv@unitv.edu
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CALVING HABITAT SELECTION OF PARTRUIENT MOOSE (Alces alces) IN NORTHERN MAINE
Brett P. Skelly
A senior thesis submitted in partial fulfillment of the requirements for the degree of
Bachelor of Science
Wildlife and Fisheries Management
At
Unity College
Spring, 2016
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Abstract
Cow moose maternal strategies for selecting calving site locations are not very well
understood and are highly variable across their current range. Moose will typically select for
areas that are secluded and mitigate the probability of predator-moose interactions.
Throughout their range moose population growth is typically limited by low calf survival due to
predation and/or limited forage available on the landscape. This project examined calving site
selection of 14 parturient moose throughout Somerset county, ME. A Brownian Bridge
Movement Model (BBMM) was used to estimate the 90% and 20% core areas of each cow,
during the period of peak parturition. The 20% core area and trajectory analysis of GPS points
were used to estimate calving sites. Both physical and vegetative parameters were collected
both spatially and on the ground to compare calving sites and randomly selected sites, within
the 90% core area for each moose. Cow moose selected for areas that offered higher
concealment cover than randomly selected sites. They also selected to calve farther from both
streams and roads, as well as on gentle slopes, and well drained areas. The results of this study
suggest that cow moose are selecting calving sites for predator avoidance features. Having a
greater understanding of calving habitat selection can be used to inform land use practices to
increase calving habitat. Increasing calving habitat can lead to increase in calf survival therefore
increasing population growth .
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ACKNOWLEDGEMENTS ·
I would like to that my advisors Drs. Brent Bibles and Stephanie Wade for all of their
input and help on developing this project. I would also like to thank the Maine Department of
Inland Fisheries and Wildlife (MEDIFW) especially Lee Kantar for allowing me to use the GPS
data from his collard moose survival study and providing me with housing in the Greenville
office for a portion of the data collection period. I would like to thank Unity College and the
Student Academic Engagement Fund (SAEF) award for providing me with the necessary monies
to conduct this research. I would like to thank Dr. Kevin Spigel for his input on the dirt
collection and processing part of the project. Finally, I would like to thank everyone else who
provided me with input and motivation along the way.
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•• Table of Contents • Introduction 9 • Background Information 9
Calf Characteristics 10
II Population Dynamics 12
Breeding Cycle 13 • Predation 14
Wolf Predation 15 • Bear Predation 15
Predation Adaptations 17 • Calving Site Classification 18
Objectives 19 • Hypotheses 20
Methods 21 • Study Area 21
Home Range Analysis 21 • Site Selection 22
Field Data Collection 23 • Soil Lab Processing 24
Spatial Data Collection 25 • Data Analysis 25
Model Selection 26 • Results 26
Discussion 28 • Conclusion 31
• Study Improvements 31
Future Work 32
I Management Implications 33
Literature Cited 39
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List of Tables
Table 1. Habitat Types
Table 2. Spatial Data Models
Table 3. In-field Data Models
List of Figures
Figure 1. Map of Study Area
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INTRODUCTION
Background Information
Moose (Alces alces) are the largest member of the deer family. They primarily occupy
the boreal forests throughout Alaska east across Canada to the island of Newfoundland, as far
south as into the Rocky Mountains, and south into New England (Reid 2006). There are four
accepted sub-species of moose throughout this range. The sub-species of moose in Maine is
the Eastern or Taiga moose. At maturity, the eastern moose stands at or greater than 1.85
meters (shoulder height) and can weigh between 360-600 kg (live weight) (Franzmann and
Schwartz 1998).
Moose are a part of the Cervidae or deer family. This family displays sexual dimorphism,
where the male is typically larger than the female. The male will also grow antlers that are
shed annually (Feldhamer et al 2007). Cervids typically have a narrow mouth which makes
them a highly selective herbivore and only their mandible contains incisors. Cervids forage
primarily on woody and herbaceous vegetation that is snipped off from between their upper lip
and lower incisors (Feldhamer et al 2007). Most Ruminantia have a unique digestive tract to
break down forage consumed and get the greatest amount of nutrition from that forage. They
have a foregut fermentation digestive system -which consists of a four chambered stomach -
which is highly specialized to obtain the highest amount of nutritional absorption from the
fermentation process (Feldhamer et al 2007). This process is a relatively slow and the animal
can only process so much forage within a period to time depending on how abrasive the forage
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being consumed is. This is why cervids must be highly selective foragers for primary high
quality forage to meet their energetic demands (Feldhamer et al 2007).
Calf Characteristics
Cow moose typically give birth to one or two calves annually. There is no evidence that
maternal mass can be used as an indicator in determining if a moose will have a singleton or
twins, and determination of the sex ratio is also not possible by any current literature
specifically examining moose. Moose calves do not exhibit any sexual dimorphism at birth in
either weight or length. However, maternal condition influence the weights of neonates and
ultimately their survival. This has been supported in an elk study that showed that the greater
the weight of the calf the greater that chance of survival during the first month of life, while
there is no data that supports this hypothesis in moose (Franzmann and Schwartz 1998).
The development of the calf begins at conception during the breeding season after the
cow has been successfully bred. There are two growth phases that moose undergo throughout
their entire life cycle. The first phase is termed as the 'self-accelerating phase of gqJwth'. This
phase is a two part cycle with the first being the development of the fetus until birth; and the
second being from birth to weaning of the calf. The second phase of growth is termed the 'self­inhibiting
phase of growth'. This phase has only one cycle that is from post weaning to death .
During this time period the rate of development of the individual decreases (Franzmann and
Schwartz 1998) .
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After parturition the calf will begin to nurse, this can occur as early as one and a half
hours after birth. The cow's milk consists of "25-32 percent total solids, 5-12 percent fat, 14-19
percent crude protein, and 2-6 percent lactose". The calf needs to consume high quality and
quantities of milk during this first developmental cycle. Calves consume 26.4 to 52.8 gallons of
milk from birth till they are weaned off mother's milk (Franzmann and Schwartz 1998). Calves
will nurse from between the cows hind legs, her flanks, and while laying down. Young calves
less than a week old will nurse from all three locations, while calves greater than a week old will
allows nurse from either between the cows hind legs or from the flanks. Between 2-3 weeks
after birth the calf will begin to consume plant matter along with milk and from 6-8 weeks the
calves' transition to consumption of mainly plant material. Weaning begins in mid-September
when suckling is discouraged by the mother and the calf is eating more plant based forage. It is
thought that the weaning process occurs because nursing becomes painful for the mother and
the calf is being increasingly more aggressive (Franzmann and Schwartz 1998).
The cow-calf pair will stay together throughout the winter months but in the spring the
cow-calf pair will separate. It has been noted that males will disperse earlier than females,
which is observed in most mammalian species. The cow will exclude the yearling (previous
year's calf) from her core area in preparation for giving birth again. The mother will forcefully
run off her offspring, but still allow them to occupy part of her home range (Edward 1983 and
Franzmann and Schwartz 1998). Yearlings will disperse to areas that have moderate habitat
quality with the fundamental needs for survival. The dispersal of offspring is thought to be a
density dependent response where a population with high density will have shorter distances
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of dispersal and an area with low density will have greater distances of dispersal (Cederlund et
al. 1987).
Population Dynamics
Population size can vary based in the birth rates, death rates, immigration, and
emigration within that population. All wildlife species have a carrying capacity (K) that is
determined by the biotic and abiotic factors in that area; the population size will fluctuate
around this carrying capacity throughout time. Most wild populations are typically limited by
the amount of quality habitat on the landscape, predation, and disease. Natality is the rate of
individuals entering the population via birth, and maximum natality is the highest input of new
individuals into the population under the best possible conditions for that species. The natality
rate is going to vary from one population to the next based on the status of that population and
the quality of habitat (Franzmann and Schwartz 1998). The quality of forage on the landscape is
going to have a bigger impact on the survival of moose due to their slow processing time of low
quality, fibrous forage .
Predation or death rate is also going to play a large role in the decline or stabilizing of
the population. These rates of predation can be either additive or compensatory mortality to
the population. The type of mortality is typically determined by the amount at which it limits
the growth of the population and ultimately the recruitment of individuals into the next age
class. Ballard (1992) defines additive mortality as an additional source of mortality; in other
words it's removing individuals that would have otherwise made it to the next time step .
Compensator mortality is defined as a form of mortality that does not limit population growth;
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it is the removal of individuals that would not have made into the next time step regardless of
predation (Ballard 1992).
Breeding Cycle
Moose, like all cervids, have a breeding season in late fall. Cow moose are polyestrous
meaning that they will have multiple estrous cycles during the breeding season. These cycles of
going into heat will continue until they become pregnant or the breeding season has ended.
They typical ovulation last anywhere from 18 to 72 hours, and range from 20-22 days between
ovulations. Once a moose is successfully bred, they have a gestation period that ranges from
216-240 days. Neonates are typically born in late spring/early summer (Franzmann and
Schwartz 1998).
Across their range the age when a cow moose first ovulates varies based on the
environmental conditions and the condition of the cow. While most cows in good quality areas
will ovulate for the first time between 1.5 and 2 years of age, moose in poor quality areas will
typically not ovulate until 3.5 years of age. While rare it is thought that moose that ovulate and
breed at a young age will be less developed (height and body weight) than moose that did not
breed at a young age. Cows that bred when young will have a stunted growth and typically do
not have as high of reproductive success throughout their life as cows that did not bred until
they were fully developed (Franzmann and Schwartz 1998) .
Ovulation rates and pregnancy rates in moose vary from one population to another but
ranges from 71 to 100 percent and 82 to 100 percent, respectively. These rates will vary as the
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population is reaching carrying capacity or exceeding carrying capacity. Cow moose will
typically have one to two calves per year but the rate at which twinning occurs is correlated
with the habitat quality and the carrying capacity of the population, triples have been observed
in wild moose populations but is rare. Barren cows are termed as an individual that do not go
into heat and therefore, will not be bred, while this is rare it does occurs (Franzmann and
Schwartz 1998).
After successful birth of a calf or calves, a new cycle starts, which consists of lactation,
weaning, dispersal, and breeding. Once the calf(s) have been born, the cow will clean her
calf(s), eat the placenta, lick up any amniotic fluid, eat soil, and the calf(s) will being to nurse.
The cleaning of the calf is the initial bonding between the cow-calf pair. This cow-calf bond is
an important part in the survival of the calf. This bond takes 7-8 days to develop and is due to a
constant contact between the cow and her calf. The calf is relatively immobile for a few days
up to the first week of life so the cow will be tied to the site until the calf is strong enough to
travel. For the first week the cow stays within visual or vocal communication with the calf,
typically within a 50 meters radius to the calf (Franzmann and Schwartz 1998).
Predation
Predation on neonate ungulates in most populations does not appear to have a
significant effect on population growth because survival of older individuals has a greater effect
on population size than neonate survival. However, in the case of moose neonate survival is a
limiting factor that decreases population growth (Patterson et al 2013). There are a few
different large mammalian predators that are attributed to preying on moose and moose calves
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throughout their range. These apex predators include wolves (Canis lupus), American black
bear (Ursus americanus), brown, and grizzly bears (Ursus arctos). While some literature
suggests the eastern coyote (Canis latrans) will predate upon moose calves, this is considered
to be a rare event. Edward (1983) suggested that predator's effects prey by preying on them
and altering prey habitat selection due to predator avoidance. When there are multiple
predators on the landscape each can have their own effect on moose and calf survival and
habitat selection. Ballard (1992) suggests that predation rates by each predator are density
dependent. This is based on the density of each predator in that area and moose densities.
When moose are at or near vegetative carrying capacity calf mortality is compensatory; this
typically occurs when moose are at high densities. When moose are at low densities Ballard
(1992) suggest that predation is additive .
Wolf Predation
Wolves are species specific predators, typically predating on ungulate species. Wolf
predation has been shown to be a limiting factor of population growth for many ungulate
populations. Wolves account for 3-9% of calf mortality (Ballard 1992). Ballard (1992) suggests
that wolves are the main predator of adult moose during the winter months when moose
mobility is low due to high snow depth. This allows for wolves to be a lot more successful
hunters than during the summer season when moose are highly mobile and able to defend
themselves .
Bear Predation
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Bears have been shown to be a significant source of calf mortality and contribute to
both additive and compensatory mortality. When moose densities are low then bear predation
on moose calves creates additive mortality (Ballard 1992). When the bears are the only
predator on the landscape predating moose calves account for 30-70% of calf mortality (Ballard
1992 and Garneau et al 2007). When other apex predators inhabit the same area as black
bears, they do not have as high of predation rates. Some studies suggest that when black bears
and grizzlies are both on the landscape they can be a significant sources of mortality and ,
become an additive source of mortality of calves (Ballard 1992). A study in Saskatchewan found
when moose densities were low mortality of moose calves was additive. When bear numbers
were reduced there was an increase in calf survival that year. Immediately following the
stopping of bear reductions calf survival rates returned to their orrginal levels prior to bear
removal (Ballard 1992).
The main predator of moose calves throughout the eastern part of their range is the
American black bear (Franzmann and Schwartz 1998 and Ballard 1992). Multiple studies
conducted have determine that bear predation can be a limiting factor on moose population
growth. Black bears are generalist omnivores that predate on both plant and animal matter.
They alter their forage selection based on what is available for them to eat on the landscape.
Following den emergence black bears primary consume highly digestible plant and animal
matter (Bastille-Rousseau et al 2010). Black bears typically forage on ·high quality vegetation
that typically emerges sooner in lowland areas therefore bear-moose interactions are higher
during this time period. Bastille-Rousseau (2010) suggested that roadsides and wetlands
offered the most amount of vegetation for bears to forage on earlier in the season, and shrub
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lands, regenerated clear-cuts, and mature conifer stands offered the lowest amount of forage
available so bears typically avoided using these areas. Bears moved from one patch of high
quantity vegetation to the next and spend shorter amounts of time foraging in these high
quantity areas. Bastille-Rousseau et al (2010) and Garnueau et al (2007) found that bears are
not actively searching for neonates and that most encounters with ungulate neonates occur
during inter-patch movements. Therefore supporting that bears are opportunistically feeding
on moose and actively searching for neonates. The habitats were bears are· foraging the most
on high quantities of forage is going to increase their probability of encountering moose that
are also selecting for areas of high forage availability (Bastille-Rousseau et al 2010 and Garneau
et al 2007).
Predation Adaptations
Neonate ungulates are most vulnerable to predation during the first month of life
(Bastille-Rousseau 2010). Therefore, most ungulate neonate species have special adaptations
for predator avoidance and hiding. While cow moose will defend their offspring, calves do not
have any adaptations for hiding from predators. Calves are termed as "laying-out", while on
the calving ground. The calf lays on its sternum with forelimbs out in front of its body and head
laid between front legs (Franzmann and Schwartz 1998).
Once the cow-calf pair leaves the calving ground, the chance of predation typically
increases due to the movement of the cow-calf pair into new areas that could be potentially
occupied by predators (Franzmann and Schwartz 1998) .
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Moose have been observed to alter their diets based on the predators in the area and
the habitat that predators typically search for food. Cow moose will move into areas for calving
purposes that are low in forage quality and quantity in the attempt to mitigate predation ·
probability. Cows in Isle Royale Wilderness National Park, Michigan were observed moving to
lake shorelines of peninsulas and islands to calve. This is interpreted to be an adaptation to
avoid predators which leads them to select for area of low forage quality and quantity (Edwards
1983).
Calving Site Classification
While moose calving selection throughout their range is being studied, only two papers
in the literature discuss calving characteristics in northeastern North America, Leptich and
Gilbert (1986) and Scarpitti et al. (2007). Prior research indicates that calving site across their
range have been characterized as secluded areas, that minimize the chance of predation, have
high quality forbs to meet the high energy demand of lactation, and are near water resources.
In Maine calving site parameters that were influential in distinguishing calving from
random sites consisted of stand density, tree size, drainage, and accompanying upland or
lowland vegetation, stand closure, and site disturbance (Leptich and Gilbert, 1986). Calving
sites have been characterized by "undisturbed and poorly drained sites often dominated by
cedar although non-forested calving sites area also represented. Typically close to water and
may have small diameter browse species present on the site." (Leptich and Gilbert, 1986).
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In New Hampshire Scarpitti et al (2007) found no significant difference between random
and calving sites in most variables they investigated but there were a few parameters that did
differ. Distance to cut/regenerated patches was twice as close for random sites compared to
calving sites. Calving locations were also 100 meters farther from roads than random sites but
this difference was not statistically significantly. There was also no difference in elevation of
calving or random sites (Scarpitti et al. 2007).
In Minnesota calving sites were characterized by (McGraw et al. 2014) forest diversity
and were typically located in bogs. The use of bogs was higher than the availability of the bog
cover and allowed calves to have high hiding cover, greater forage, and access to water. As the
cow calf pair move more the use of mixed and young and regenerating forests increased
(McGraw et al. 2007).
In Alaska calving sites were distinguished by 3 three parameters: forage, aspect, and
visibility (Bowyer et al,. 1999). The amount of forage was significantly greater at birth sites than
random sites, this was attributed to the amount of willow at the birth sites. Birth sites were
also 96 meters higher than random sites, which would allow the cow to have greater visibility at
the calving site. The majority of calving sites were located on southeastern exposures which
can be attributed to green up occurring earlier in spring on aspects in that direction. However,
cows did not calve closer to human developments compared to randomly selected sites
(Bowyer et al. 2007).
Objectives
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The objective of this study are (1) identify different habitat types selected by cow moose
as calving sites; (2) identify land use practices that influence cow moose calving habitat; (3)
inform managers on habitats used by cow moose as birth sites and during immediate post­parturition;
(4) compare results from other studies looking at cow moose birth site selection,
particularly in northeastern North America
Hypotheses
Cow moose will select calving locations that will decrease the probability of predator
interaction but will still offer attainable browse to meet the high energetic demands of
lactation. I predict that cow moose will therefore select for sites that offer greater
concealment cover for their neonates than randomly selected sites. Increase in concealment
cover will provide the calf with more hiding cover. This is going to decrease the probability of
predators detecting the calf. Calving sites will be characterized by being closer to water
resources and farther from roads than randomly selected sites. Increasing the distance from
road is going to decrease the probability of predator interactions that use roads as travel
corridors. A decrease in the distance from water resources is going to allow for water to be
available when on the calving ground. Large water bodies can also be escape routes for the
cow-calf pair to evade predators. Finally, cows are going to select for areas that are well
drained and have a higher diversity of habitat types than randomly selected sites. Well drained
locations are going to allow the calf to lay on the calving ground and not be wet. This is going
to help decrease the probability of the calf getting sick. Selection for areas that have a higher
diversity of habitat types is going to increase the amount of forage available to cows. This
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increase is going to allow cows to have more forage for a longer time when on the calving
grounds as oppose to homogeneous landscapes.
METHODS
Study Area
All study animals were located in Somerset County, ME -except for one cow whose
home range was half in Piscataquis County. The study area consists of both public and private
lands in Northwestern Maine (Figure 1). The cow moose home ranges were established in the
Western Moosehead region over to Jackman, and as far South as The Forks, and east to Shirley
Mills. This area in Maine is heavily logged for commercial products; therefore, the landscape is
continually changing. The largest logging company in the area is Plum Creek; they own 861,000
acres throughout Maine with 363,000 acres in the Moosehead Region (Plumcreek.com).
Home Range Analysis
The Maine Department of Inland Fisheries and Wildlife (MDIFW) deployed Vectronics
Vertex Survey GPS collars on adult and juvenile moose in two study areas {Somerset county and
northern Aroostook county). MDIFW started deploying these collars in 2014 and have 149
moose collared between these two study areas. The GPS collars are programed to take
locations on a twelve hour interval at 9:00 and 21:00. These collars also collect activity data
that measures the x, y, and z movement of the individual. Home range~ were established using
a Brownian bridge movement model (BBMM) (Horne et al 2007 and Kranstauber et al 2012).
The BBMM allows for greater accuracy when estimating home ranges and core areas because it
takes into account the distance moved between observations, activity during that time period,
and the error associated with the observation {Horne et. al., 2007).
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I developed home range estimates for 14 adult cows that were possibly pregnant.
Home range analysis was done in program R using the adehabitat package. Missed GPS fixes
were recorded as zeros to maintain a constant time interval between points. I selected a time
frame from the 25 April till the 25 June. This allowed for the home ranges to be created that
explicitly showed the time period leading up to parturition and post parturition. Next, I used a
trajectory graph to assess the distance movements of moose between consecutive locations.
The trajectory of a parturient moose is characterized by a long distance movement then a
reduction in movement for approximately a week (McGraw et. al., 2014 and Severud et. al.,
2015}. Next, I created a utilization distribution to locate the areas of highest probability for that
individual. Using the BBMM I created a 90% and 20% core areas for each individual. Finally, I
imported the core areas and associated points into ARCmap for further analysis.
Once all of the shapefiles were loaded into ARCmap the points for each moose and the
20% kernels were further examined. Using the trajectory projections, GPS points, and 20% core
area I located the specific kernel that was used for calving. This helped to visually see the
movements of each individual moose before entering the 20% kernel and once in the kernel.
After determining the 20% kernel used by the moose two random points were assigned to both
that 20% kernel and the 90% kernel.
Site selection
When determine sample locations I assigning head and tails to a set of coordinates for
the calving sites and random sites. Then I flipped a coin to determine which site was sample.
Field Data Collection
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The same data were collected at both sites (birth and random) to allow for comparative
analysis of the composition of each site. I collected was both physical and vegetative
measurements. The data collected at each site consisted of percent canopy cover;
concealment cover determined using a ball-staff method; total basal area (BA) with a basal area
factor (BAF) of 10; measurement of the diameter at breast height (DBH) and record of tree
species; the number of snags (DBH >Scm) within a 10 meter radius of plot center; soil sample;
records of dominant canopy and tree species; and dominant habitat type around plot center.
The dominant habitat type was visually assessed at each site and a predesignated
habitat classification was selected (Table 1). The dominant tree species and canopy species
were visually assessed at each site. The canopy species was determined by the amount of leaf
cover in the upper level of the canopy. This excluded younger and lower height trees from
being considered as canopy species.
The percent canopy cover was then determined using a concave spherical densiometer
10 meters from plot center in all four cardinal directions (north, south, east, and west) (Field
Procedures 2009). The percentages of each direction were averaged to obtain a single percent
canopy cover for that site. Next, the concealment cover was estimated using a ball-staff
method (Collins and Becker 2001). Two balls (10 centimeter in diameter) were placed on a 1.5
meter rod; one ball was positioned at 0.5 meter and the other at 1.0 meters above the ground,
and both were viewed at 10 and 25 meters in all four cardinal directions. Then the visibility of
the line where the ball and rod meet was determined (Collins and Backer 2001).
Basal area was determined using a Cruz-all prism with a BAF of ten measured from plot
center (Robertson 2000). The trees that were counted in then had the DBH and species of the
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tree recorded. The number of snags (DBH>Scm) were visually counted within a ten meter
radius of plot center (Robertson 2000).
Soil samples were collected at 10 meters from plot center in all four cardinal directions
if soils could be extracted. First, the area around the site was cleared of leaf litter. Then, the
soil core was drove 8 centimeters into the substrate. Once the correct depth was reached the
area around the core was excavated so a piece of metal could be inserted at the bottom of the
core to ensure no soil was lost upon extraction. The soil was then put into a Whir-pack and
labeled with corresponding site information such as moose id, site classification, date, and
direction from plot center. The soil weights were immediately recorded and the volume of the
soil core was also recorded.
Soil Lab Processing
Once all of the soil samples were collected from in the field, they were processed in the
Unity College Geoscience Lab. Soil processing was conducted to estimate the average soil
porosity per site. The formula used for calculating porosity is as follows Pdry = Pp(l-n), where
Pdry is dry bulk density (Pdry =(Ms/Vt), Pp is the density of soil particle, and n is porosity (Ward
and Trimble 2004). First, beakers were labeled with site information and weighted empty.
Then, soil was transferred from the Whir-pack to a labeled beaker and weighed again. Next,
the beakers were placed in a furnace at a temperature of 105 degrees Celsius for 24 hours. The
drying period removed all moisture from the soil, and the beakers of soil were reweighted. This
allowed for the dry bulk density to be calculated using the dry weight and volume from the field
core. Each individual soil sample was then placed into a lOOOml beaker with 350ml of water .
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The final volume of water was recorded after the soil was added. Finally, after rearranging the
equation for dry bulk density, porosity values were calculated (Ward and Trimble 2004).
Spatial Data Collection
After completion of all the field data collection, the points sampled in the field were
then put into ARCmap. Once in ARCmap, the elevation, slope, and aspect were derived from a
digital elevation model. The distance to nearest road, stream, and open body of water was
calculated using the near tool in the Arctoolbox. The percent of each vegetation type (Macro
layer for Maine TNC) at each site was determined with a one hundred meter buffer around
both calving and random site.
Elevation, slope, and aspect were all derived from a digital elevation model for Somerset
County from the Maine GIS database (Maine Office of GIS). The distance to nearest feature tool
in ARC tool box was used to calculate the distance to nearest road, stream, and open body of
water from each sampled site.
The use of a Shannon-Weiner index was used to calculate the species richness of each
site within a 100 meter radius of each sampled site (Silvy 2012). Using the buffered calving and
random sites I extracted the different habitat types. Then, using the area of each habitat type
within the buffer I calculated Shannon-Weiner index value. This allowed for habitat diversity to
be determined between calving and random sites.
Data Analysis
The data for this study was analyze9 using R studio to develop and run models to test
the relationships between site parameters and the likelihood of it being either a birth site or a
random site. This study used generalized linear models (glm) to determine the relationship
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between site classification and parameters collected at each site. Spatial and in-field collected
data were analyzed separately because of differences in sample size (spatial n=14 and in-field
n=lO). Spatial data models were run, and top performing models helped to inform model
construction for in-field measured parameters. Along with the models constructed a null model
was run to determine the predicting power of all parameters into one model.
Model Selection
Model comparison was done by evaluating AICc values for each model constructed. The
lower the Al Cc value, the better fit the model had to the data. The top performing parameters
(those that did not encompass 0 in their confidence intervals) were rerun using an interaction
model with another high performing parameter. The use of interaction models allowed for a
more complex analysis of multiple parameters to predict site classification in a nonparallel
distinction.
RESULTS
Sampling of both spatial and In-field data took place during the fall of the 2015. There
were 14 parturient moose sampled spatially and of those 10 were sampled In-field. Each birth
site sampled had an associated random site sampled for each moose.
All of the models that were considered further had to be within 2 AIC of the top
preforming model. Any model that was within 2 AIC showed that it fit the data and could have
explanatory power when distinguishing calving locations from random locations. The spatial
model results are in Table 2 and the In-field model results are in Table 3. The spatial parameter
that had the most explanatory power for the probability of predicting a birth site was distance
from road. This had a positive relationship whereas the distance from road increases the
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probability of a calving site also increases. The next top model was distance to stream. This
model had a positive relationship whereas the distance from stream increases the probability of
a calving site occurring also increased. When slope was tested it was the next best performing
model. There was a negative relationship shown, when slope decreased and became more flat
the probability of it being a calving site increased. The next model was an interaction effect
between distance to stream and slope. This model showed that as distance from stream and
slope increase the probability of a calving site occurring decreases. Natural drainage class was
then tested resulting in a positive relationship. As the soil class increase (better drained) the
probability of a calving site occurring will also increase. An additive model was constructed
testing the distance to road and distance to stream. This model showed that as the distance
from both road and stream river increases the probability of a calving site occurring also
increases. The final model that is within 2 AIC was an interaction effect between distance from
road and slope. This model showed that as the distance from road increases and the slope
decreases the probability of a calving site occurring will increase. Therefore, the parameters
that are most influential in determining calving sites are distance to road, distance to stream,
slope, and natural drainage class.
The In-field model testing had one model that outperformed all other models in this
data set. This model showed that as obstruction at a height of 0.5 meter at a distance of 10
meters from plot center increase the probability of calving will also increase. All other models
were not within 2 AIC of this model. However, the top six models tested are presented in Table
3.
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In the spatially explicit data there was no support for predicting calving sites using
elevation, aspect, habitat classification, Shannon-Weiner Index, and distance to water bodies.
However, most of the sampled locations fell in boreal upland forests, northern hardwood
forests, and northern swamps. The percent canopy cover, total basal area, species specific
basal area, and total number of snags showed no correlation to predicting calving site selection.
DISCUSSION
The results show that calving sites in Northern Maine are characterized by having high
obstruction levels around the birth site at 0.5 meters above the ground. They are also
characterized as being located far from roads and streams, on gentle slopes, and typically on
well drained soils.
The use of areas by parturient moose that are characterized as having high concealment
cover at a 0.5 meter height can be seen as a way to make the calf or calves harder to detect by
predators. Therefore, it would increase the calf's survivorship while on the calving grounds in
the chance of having an encounter with a predator. The importance of concealment cover
when on the calving grounds suggests that cows are expressing a predator avoidance
mechanism. This study was unable to directly measure if forage availability (due to time of
sampling) was a possible driving force in calving site selection. This could have been an artifact
of the high obstruction levels around the birth site compared to random sites. These results
support what Langley and Pletscher (1994) found that concealment cover from 0-1 meter to be
an important characteristic of calving sites in Montana and Southeastern British Columbia.
However, Bowyer et al. (1999), Leptich and Gilbert (1986), and Scarpitti et al. (2007) found that
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were was not a significant difference in concealment cover between birth sites and random
sites in Alaska, Northern Maine, and New Hampshire, respectively. The data collected support
the first prediction that calving sites are characterized as having higher concealment cover than
randomly selected sites.
Calving site were also more likely to occur as the distance from streams increased, but
the distance from water bodies was not an explanatory variable in calving site selection.
Distance from flowing water can be explained as a predator avoidance mechanism because
riparian habitats have been shown as predator foraging corridors. This is due to the amount
and diversity of vegetation emerging in these areas that are available for bears to forage on
after emerging from denning (Bastille-Rousseau et al. 2010).
My result found that there was not relationship between the distance to water body
and calving site selection therefor supporting Scarpitti et al. (2007) and Leptich and Gilbert
(1986). However, the results support that distance from streams is an influential characteristic
in calving habitat selection. This does not support my prediction that calving site selection will
be closer to water bodies because there was not a difference in distance to water bodies
between calving and random locations. The density of water bodies in each study area could
have been influencing the difference in distance from both calving and random sites.
The results suggest that calving sites were characterized as being farther from roads
than randomly selected sites. These results support the prediction that calving sites will be
father from roads than randomly selected sites. However, Leptich and Gilbert (1986) and
Scarpitti et al. (2007) found no difference in the distance from roads to birth or random sites.
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This could be a predator avoidance behavior because they associate these features with travel
or hunting corridors for black bears (Bastille-Rousseau et al. 2010). The difference in the
distance from roads can be a function of the amount and density of roads in each study area
and the amount of newly developing roads in area with heavy logging. Bastille-Rousseau et al.
2010, found that along paved and unpaved roads there was a greater amount of vegetation
abundance that black bears foraged along.
Slope was found to have predicting power between birth and random sites. Cow
selected for areas that had gentler slopes than randomly selected sites. Therefore it supports
the prediction made that cows will select for areas with decreased slope than randomly
selected sites. These findings contrasts with Gilbert and Leptich (1986) and Scarpitti et al.
(2007) who found no significant difference between slope at birth and random sites. This could
be due to the more mountainous region of this study compared to previous studies. A decrease
in the amount of slope would aid the neonate in mobility around the calving site therefore
allowing it to better evade predators if they came into contact.
The natural drainage class had some predicting power suggesting that the better
drained soil class the high probability of calving. These results support the prediction made
that cows are going to select for areas with well drained soils compared to randomly selected
sites. This is contrary to what Leptich and Gilbert (1986) found that suggests calving sites were
classified as being poorly drained sites. Sites that are not will drained would have a higher
probability of containing aquatic vegetation and browse that would emerge sooner on spring as
compared to dryer sites. Bears will typically forage on newly emerging vegetation therefore
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feeding in sites that would be wet during late spring. Bears will typically use wetter locations as
travel corridors because it allows them to cool off their pads to help regulate body
temperature.
This study was unable to detect any major vegetation classifications that are selected by
moose for calving. This could a product of the heterogeneous landscape where this study was
conducted. Land use practices such as logging also help to increase the amount of
heterogeneity on the landscape. Therefore, the prediction that cows are going to select for
calving sites that have high forest heterogeneity was not supported by the results.
CONCLUSION
The results of this study show that moose are selecting for physical and vegetative
parameters that are going to mitigate the probability of predator-moose interactions when on
the calving grounds. The parameters that they selected for are high concealment cover,
increased distance from both roads and streams, gentler slopes, and well drained soils.
Study Improvements
There are a few areas where this study could be improved. The first improvement
would be to have better estimations of calving locations. This could be accomplished my
conducting walk-in surveys to observe cow/calf pairs at the birth site. Deploying vaginal
implant transmitters (VIT) into the birth cannel of pregnant moose would allow for the most
accurate estimation of were parturition took place. However, this would add in extra expenses
for purchasing the VITs and monitoring them till parturition occurs. A cheaper solution to this
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problem would be to increase the amount of fixes that are taken by the collar around the time
of parturition to be able to identify large movements sooner and see localizations more
accurately. This would also allow for more chances for fixes to be taken. At the current 12
hour interval if a single fix is missed the individual is not accounted for a 24 hour period.
Another improvement would be conducting sampling of all points after the cow-calf pair
leaves the calving grounds. This would allow for more accurate measures of concealment cover
and canopy cover at both sites. Sampling after the pair leave would allow for identification of
important browse species and allow for sampling of available forage at the birth site.
The use of a different way to extract the soil cores would have allowed for greater
accuracy when classifying soils types at each site. The core that was used did not allow for easy
extraction from the core into the sample bags. This could have led to the loss of soil and
therefore inaccuracy in the sampling method. The diameter of the core also made it more
difficult to extract soil at some sites due to the amount of rock and/or root obstructions which
led to no soil being able to be extracted in some situations. The use of a soil probe would have
allowed for soil extraction to be more accurate and easiertransportation to the sample bags
with less loss of soil. This would have also allowed for fewer roots to be in each sample making
the displacement values more accurate.
Future work
If this study was to be conducted again I would suggest making all of the changes in the
above section to allow for more accurate data to be collected. I would also suggest trying to
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collect an index of forage at each site to better address if the cow was selection for forage
availability or predator avoidance. I would collect all of the same spatial data with the addition
of trying to address calculating the distance to hard edges and logging sites. Estimating the
distance to logging also trying to determine the age of the cut and if there is any relationship to
cut age and probability of use.
Increasing the sample size will help to support the conclusions drawn. Including moose
over consecutive years to assess whether or not cows are selection for the same physical and
vegetative features form one year to the next.
Management Implications
This research can be used to inform managers and foresters on how land use changes
can effect calving habitat selection. Calving site selection is not random therefore the cow is
selecting for features that are both physical and vegetative to increase the probability of
survival while the cow-calf pair is on the calving grounds. If calving habitat is limited this could
lead to a decrease in the survival of calves and an overall decrease of adult moose being
recruited into the population. Calf survival is in most areas a limiting factor to population
growth and therefore could be what is keeping the moose population below its vegetative
carrying capacity. If there is an increase in the amount of preferred calving habitat this could
increase calf survival and therefore increase moose population size.
The use of BBMM in determining calving sites could be implemented to save on costs of
walking in on cow-calf pairs and risking maternal abandonment. This could also eliminate the
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use of VITs in certain cases and decrease the cost of buying, deploying, and continually
monitoring VITs frequencies.
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Figures/ Tables
Figure 1. Map of the birth and random sites sampled.
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Habitat Types:
Table 1. The habitat type in field classification scheme with associated acronyms.
Deciduous upland dcup
Deciduous lowland dell
Coniferous upland cf up
Coniferous lowland cfll
Mixed upland mxup
Mixed lowland mxll
Cut-regeneration ctrg
Other other
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Table 2 Spatial data Models
Model Spatial 0-1 (Random-
Testing Data Birth)
Model Model Y-Response_Var Variable(s) AIC Confidence
Rank Code (Variable)
1 msl Site Dis_Rd 40.66 (-0.0003 I 0.0022)
2 ms7 Site Dis_SR 41.47 (-0.0009 I 0.0038)
3 msll Site Slope 41.76 (-0.4129 I 0.0833)
4 ms15 Site Dis_SR * Slope 42.10 (-0.0001I0.0095)
5 ms2 Site Natural.drainage.Class* 42.13 (-0.3114 I 0.8194)
6 ms8 Site Dis_SR + Dis_Rd 42.24 (-0.0017 I 0.0035)
7 ms14 Site Dis_Rd. * Slope 42.56 (-0.0011, 0.0031)
*Natural drainage class is very poorly drained to well drained {1-6}
msl: As the distance to road increases the probability of a calving site is going to increase.
ms7: As the distance to SR increase the probability of a calving site is going to increase.
msll: As slope increase the probability of a calving site is going to decrease.
Confidence
(Variable)
(-0.4633 I
0.5830)
(-0.0005 I
0.0022)
(-0.9020 I
0.3231)
mslS: As distance to SR increase and slope increase the probability of a calving site is going to decrease.
ms2: As soils become more well drained the probability of a calving location is also going to increase.
ms8: As both distance to rd and SR increase the probability of a calving location will also increase.
Confidence
(Variable)
(-0.0014 I 0.0003)
(-0.0003 I 0.0005)
ms14: As distance to rd increase and slope decreases there is an increase in the probability of a calving location occurring.
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Table 3 In-field data Models
Model ln-field_Data 0-1 (Random-Birth)
Testing
Model Rank Model Code Y-Response_ Var Variable(s) AIC Confidence Confidence Confidence
(Intercept) (Variable) (Variable)
1 mfll Site Ball_1_10M 24.58 (1.0584 I 10.9281) (-14.1168 J
-1.5653)
3 mf13 Site Ball_1_25M 27.55 (-0.3637 I 2.2543) (-8.7128 I
0.1380)
4 mf14 Site Ball_1_10M 28.48 (-0.5431I15.6905) (-20.9618 J (-22.2648 J
*Ball 2 lOM 0.8676) 24.1661)
5 mf15 Site SM 29.7 (-0.6741 I 1.3532) (-0.1231 I
0.0126)
6 mf12 Site Ball_2_10M 30.88 (-0.8924 I 2.0576) (-5.2804 I
1.9153)
Ball-Staff readings: Ba/11 (O.Sm) Ba/12 {1 .0m) line observed no (O) yes (1)
mfll: At a distance of 10m as site obstruction increase at a height of 0.5m the probability of it being a calving site is going to
increase.
mf8: These are highly correlated variable so they are very explanatory when all combined.
mf13: At a distance of 25m as site obstruction increase at a height of 0.5m the probability of it being a calving site is going to
increase.
Confidence
(Variable)
(-26.3945 I
27.8144)
mf14: At 10m as obstruction increases at O.Sm of height but decrease at 1.0m of height the probability of it being a calving site will
increase
mflS: As sugar maple DBH decreases probability of calving location increases
mf12: As obstructions decrease at lm of height at a distance of 10m the probability of calving will increase.
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Literature Cited
Addison E. M., D. J. Smith, R. F. Mclaughlin, D. J. H. Fraser, and D. G. Joachim. 1990. Calving
Sites of Moose in Central Ontario 26: 142-153.
Bastille-Rousseau G., D. Fortin, C. Dussault, R. Courtois, and J.P. Ouellet. 2010. Foraging
Strategies by Omnivores: are Black Bears Actively Searching for Ungulate Neonates or
are They Simply Opportunistic Predators? Ecography 1-9.
Bowyer T. R., V. V. Ballenberghe, J. G. Kie, and J. A. K. Maier. 1999. Birth-Site Selection by
Alaskan Moose: Maternal Strategies for Coping with a Risky Environment. Journal of
Mammalogy 80: 1070-1083.
Cederlund G., F. Sandegren, and L Larsson. 1987. Movement of Female Moose and Dispersal of
Their Offspring. The Journal of Wildlife Management 51: 342-352.
Collins W. B. and E. F. Becker. 2001. Estimation of Horizontal Cover. Journal of Range
Management 54: 67-70.
Edwards J. 1983. Diet Shifts in Moose Due to Predator Avoidance. Oecologia 60: 185-189.
39
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Feldhamer G. A., L. C. Drickamer, S. H. Vessey, J. F. Merritt, C. Krajewski. 2007. Mammalogy:
adaptation, diversity, and ecology. The Johns Hopkins University Press 3.rd ed.: 384-400.
Field Procedures. 2009. Canopy Closure (single point). Field Procedures 11: 1-2.
Franzmann A. W. and C. C. Schwartz. 1998. Ecology and Management of the North American
Moose. Smithsonian Institution Press.
Horne J. S., E. 0. Garton, S. M. Kone, and J. S. Lewis. 2007. Analyzing Animal Movements Using
Brownian Bridges. Ecology 88: 2354-2363.
Garneau D.E., T. Boudreau, M. Keech, and E. Post. 2007. Black bear movements and habitat use
during a critical period for moose calves. Mammalian Biology 73: 85-92.
Kranstauber B., R. Kays, S. D. La Point, M. Wikelski, and K. Safi. 2012. A dynamic Brownian bridge
movement model to estimate utilization distribu.tion for heterogeneous animal
movements. Journal of Animal Ecology 81: 738-746.
Langley M.A. and D. H. Pletscher. 1994. Calving Areas of Moose in Northwestern Montana and
Southern British Columbia 30: 127-135
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Leptich D. J. and J. R. Gilbert. 1986. Characteristics of Moose Calving Sites in Northern Maine as
Determined by Multivariate Analysis: A Preliminary Investigation. Alces 22: 69-81.
McGraw A. M., J. Terry, and R. Moen. 2014. Pre-Parturition Movement Patterns and Birth Site
Characteristics of Moose in Northeast Minnesota. Alces 50: 93-103.
Office of Information Technology. 2011. Maine Office of GIS Data Catalog.
http://www.maine.gov/megis/catalog/
Patterson B.R., J.F. Benson, K.R. Middel, K.J. Mills, A. Silver, M.E. Obbard. 2013. Moose Calf
Mortality in Central Ontario, Canada. The Journal of Wildlife Management. 77: 832-841.
Plum Creek timber Company, Inc. 2016. Plum Creek in Maine.
http://www.plumcreek.com/about/our-land-working-forests/maine
Reid F. A. 2006. A Field Guild to North American Mammals. Houghton Mifflin Company 4: 497-
498.
Robertson D.F. 2000. Timber Cruising Handbook. United States Department of Agriculture.
2409.12.
41
Scarpitti D. L., P. J. Perkins, and A. R. Musante. 2007. Characteristics of Neonatal Moose Habitat
in Northern New Hampshire. Alces 43: 29-38.
Severud W. J., G. DelGiudice, T. R. Obermoller, T. A. Enright, R. G. Wright, and J. D. Forester.
2015. Using GPS Collars to Determine Parturition and Cause-Specific Mortality of Moose
Calves. Wildlife Society Bulletin 39: 616-625.
Silvy N. J. 2012. Wildlife Techniques Manual Research. The Johns Hopkins University Press. 60-
61.
Ward A.D. and S.W. Trimble. 2004. Environmental Hydrology. Lewis Publishers 2:55-82.
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CALVING HABITAT SELECTION OF PARTRUIENT MOOSE (Alces alces) IN NORTHERN MAINE
Brett P. Skelly
A senior thesis submitted in partial fulfillment of the requirements for the degree of
Bachelor of Science
Wildlife and Fisheries Management
At
Unity College
Spring, 2016
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CALVING HABITAT SELECTION OF PARTRUIENT MOOSE (Alces alces) IN NORTHERN MAINE
Brett P. Skelly
A senior thesis completed under the supervision of
Advisor Signature Advisor Signature
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Electronic Thesis Release Form
Student name: Brett Skelly __________________________ _
Student ID: _00000020041 Degree in _Wildlife and Fisheries Management (discipline)
TITLE:_ CALVING HABITAT SELECTION OF PARTRUIENT MOOSE (Alces alces) IN NORTHERN
MAINE~~~~~~~~~~~~~~~~~~~~~~~~
I agree to allow the Dorothy Webb Quimby Library, Unity College to serve as the institutional repository of
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I hereby grant to Unity College and its agents the non-exclusive, worldwide right to reproduce, distribute,
display and transmit my thesis or dissertation in such tangible and electronic formats as may be in existence
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I certify that the version I have submitted is the same version that was approved by the appropriate college
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I affirm that the content of my submission does not, to the best of my knowledge, infringe upon anyone's
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My signature on this form signi/i~s !JJ9~Jpcknowledge and agree to these terms.
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Please provide a copy of your thesis along with a signed hard copy of this form to:
Dorothy Webb Quimby Library Unity College 90 Quaker Hill Road Unity, ME 04988 207.948.9178 librarv@unitv.edu
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CALVING HABITAT SELECTION OF PARTRUIENT MOOSE (Alces alces) IN NORTHERN MAINE
Brett P. Skelly
A senior thesis submitted in partial fulfillment of the requirements for the degree of
Bachelor of Science
Wildlife and Fisheries Management
At
Unity College
Spring, 2016
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Abstract
Cow moose maternal strategies for selecting calving site locations are not very well
understood and are highly variable across their current range. Moose will typically select for
areas that are secluded and mitigate the probability of predator-moose interactions.
Throughout their range moose population growth is typically limited by low calf survival due to
predation and/or limited forage available on the landscape. This project examined calving site
selection of 14 parturient moose throughout Somerset county, ME. A Brownian Bridge
Movement Model (BBMM) was used to estimate the 90% and 20% core areas of each cow,
during the period of peak parturition. The 20% core area and trajectory analysis of GPS points
were used to estimate calving sites. Both physical and vegetative parameters were collected
both spatially and on the ground to compare calving sites and randomly selected sites, within
the 90% core area for each moose. Cow moose selected for areas that offered higher
concealment cover than randomly selected sites. They also selected to calve farther from both
streams and roads, as well as on gentle slopes, and well drained areas. The results of this study
suggest that cow moose are selecting calving sites for predator avoidance features. Having a
greater understanding of calving habitat selection can be used to inform land use practices to
increase calving habitat. Increasing calving habitat can lead to increase in calf survival therefore
increasing population growth .
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ACKNOWLEDGEMENTS ·
I would like to that my advisors Drs. Brent Bibles and Stephanie Wade for all of their
input and help on developing this project. I would also like to thank the Maine Department of
Inland Fisheries and Wildlife (MEDIFW) especially Lee Kantar for allowing me to use the GPS
data from his collard moose survival study and providing me with housing in the Greenville
office for a portion of the data collection period. I would like to thank Unity College and the
Student Academic Engagement Fund (SAEF) award for providing me with the necessary monies
to conduct this research. I would like to thank Dr. Kevin Spigel for his input on the dirt
collection and processing part of the project. Finally, I would like to thank everyone else who
provided me with input and motivation along the way.
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•• Table of Contents • Introduction 9 • Background Information 9
Calf Characteristics 10
II Population Dynamics 12
Breeding Cycle 13 • Predation 14
Wolf Predation 15 • Bear Predation 15
Predation Adaptations 17 • Calving Site Classification 18
Objectives 19 • Hypotheses 20
Methods 21 • Study Area 21
Home Range Analysis 21 • Site Selection 22
Field Data Collection 23 • Soil Lab Processing 24
Spatial Data Collection 25 • Data Analysis 25
Model Selection 26 • Results 26
Discussion 28 • Conclusion 31
• Study Improvements 31
Future Work 32
I Management Implications 33
Literature Cited 39
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List of Tables
Table 1. Habitat Types
Table 2. Spatial Data Models
Table 3. In-field Data Models
List of Figures
Figure 1. Map of Study Area
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INTRODUCTION
Background Information
Moose (Alces alces) are the largest member of the deer family. They primarily occupy
the boreal forests throughout Alaska east across Canada to the island of Newfoundland, as far
south as into the Rocky Mountains, and south into New England (Reid 2006). There are four
accepted sub-species of moose throughout this range. The sub-species of moose in Maine is
the Eastern or Taiga moose. At maturity, the eastern moose stands at or greater than 1.85
meters (shoulder height) and can weigh between 360-600 kg (live weight) (Franzmann and
Schwartz 1998).
Moose are a part of the Cervidae or deer family. This family displays sexual dimorphism,
where the male is typically larger than the female. The male will also grow antlers that are
shed annually (Feldhamer et al 2007). Cervids typically have a narrow mouth which makes
them a highly selective herbivore and only their mandible contains incisors. Cervids forage
primarily on woody and herbaceous vegetation that is snipped off from between their upper lip
and lower incisors (Feldhamer et al 2007). Most Ruminantia have a unique digestive tract to
break down forage consumed and get the greatest amount of nutrition from that forage. They
have a foregut fermentation digestive system -which consists of a four chambered stomach -
which is highly specialized to obtain the highest amount of nutritional absorption from the
fermentation process (Feldhamer et al 2007). This process is a relatively slow and the animal
can only process so much forage within a period to time depending on how abrasive the forage
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being consumed is. This is why cervids must be highly selective foragers for primary high
quality forage to meet their energetic demands (Feldhamer et al 2007).
Calf Characteristics
Cow moose typically give birth to one or two calves annually. There is no evidence that
maternal mass can be used as an indicator in determining if a moose will have a singleton or
twins, and determination of the sex ratio is also not possible by any current literature
specifically examining moose. Moose calves do not exhibit any sexual dimorphism at birth in
either weight or length. However, maternal condition influence the weights of neonates and
ultimately their survival. This has been supported in an elk study that showed that the greater
the weight of the calf the greater that chance of survival during the first month of life, while
there is no data that supports this hypothesis in moose (Franzmann and Schwartz 1998).
The development of the calf begins at conception during the breeding season after the
cow has been successfully bred. There are two growth phases that moose undergo throughout
their entire life cycle. The first phase is termed as the 'self-accelerating phase of gqJwth'. This
phase is a two part cycle with the first being the development of the fetus until birth; and the
second being from birth to weaning of the calf. The second phase of growth is termed the 'self­inhibiting
phase of growth'. This phase has only one cycle that is from post weaning to death .
During this time period the rate of development of the individual decreases (Franzmann and
Schwartz 1998) .
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After parturition the calf will begin to nurse, this can occur as early as one and a half
hours after birth. The cow's milk consists of "25-32 percent total solids, 5-12 percent fat, 14-19
percent crude protein, and 2-6 percent lactose". The calf needs to consume high quality and
quantities of milk during this first developmental cycle. Calves consume 26.4 to 52.8 gallons of
milk from birth till they are weaned off mother's milk (Franzmann and Schwartz 1998). Calves
will nurse from between the cows hind legs, her flanks, and while laying down. Young calves
less than a week old will nurse from all three locations, while calves greater than a week old will
allows nurse from either between the cows hind legs or from the flanks. Between 2-3 weeks
after birth the calf will begin to consume plant matter along with milk and from 6-8 weeks the
calves' transition to consumption of mainly plant material. Weaning begins in mid-September
when suckling is discouraged by the mother and the calf is eating more plant based forage. It is
thought that the weaning process occurs because nursing becomes painful for the mother and
the calf is being increasingly more aggressive (Franzmann and Schwartz 1998).
The cow-calf pair will stay together throughout the winter months but in the spring the
cow-calf pair will separate. It has been noted that males will disperse earlier than females,
which is observed in most mammalian species. The cow will exclude the yearling (previous
year's calf) from her core area in preparation for giving birth again. The mother will forcefully
run off her offspring, but still allow them to occupy part of her home range (Edward 1983 and
Franzmann and Schwartz 1998). Yearlings will disperse to areas that have moderate habitat
quality with the fundamental needs for survival. The dispersal of offspring is thought to be a
density dependent response where a population with high density will have shorter distances
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of dispersal and an area with low density will have greater distances of dispersal (Cederlund et
al. 1987).
Population Dynamics
Population size can vary based in the birth rates, death rates, immigration, and
emigration within that population. All wildlife species have a carrying capacity (K) that is
determined by the biotic and abiotic factors in that area; the population size will fluctuate
around this carrying capacity throughout time. Most wild populations are typically limited by
the amount of quality habitat on the landscape, predation, and disease. Natality is the rate of
individuals entering the population via birth, and maximum natality is the highest input of new
individuals into the population under the best possible conditions for that species. The natality
rate is going to vary from one population to the next based on the status of that population and
the quality of habitat (Franzmann and Schwartz 1998). The quality of forage on the landscape is
going to have a bigger impact on the survival of moose due to their slow processing time of low
quality, fibrous forage .
Predation or death rate is also going to play a large role in the decline or stabilizing of
the population. These rates of predation can be either additive or compensatory mortality to
the population. The type of mortality is typically determined by the amount at which it limits
the growth of the population and ultimately the recruitment of individuals into the next age
class. Ballard (1992) defines additive mortality as an additional source of mortality; in other
words it's removing individuals that would have otherwise made it to the next time step .
Compensator mortality is defined as a form of mortality that does not limit population growth;
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it is the removal of individuals that would not have made into the next time step regardless of
predation (Ballard 1992).
Breeding Cycle
Moose, like all cervids, have a breeding season in late fall. Cow moose are polyestrous
meaning that they will have multiple estrous cycles during the breeding season. These cycles of
going into heat will continue until they become pregnant or the breeding season has ended.
They typical ovulation last anywhere from 18 to 72 hours, and range from 20-22 days between
ovulations. Once a moose is successfully bred, they have a gestation period that ranges from
216-240 days. Neonates are typically born in late spring/early summer (Franzmann and
Schwartz 1998).
Across their range the age when a cow moose first ovulates varies based on the
environmental conditions and the condition of the cow. While most cows in good quality areas
will ovulate for the first time between 1.5 and 2 years of age, moose in poor quality areas will
typically not ovulate until 3.5 years of age. While rare it is thought that moose that ovulate and
breed at a young age will be less developed (height and body weight) than moose that did not
breed at a young age. Cows that bred when young will have a stunted growth and typically do
not have as high of reproductive success throughout their life as cows that did not bred until
they were fully developed (Franzmann and Schwartz 1998) .
Ovulation rates and pregnancy rates in moose vary from one population to another but
ranges from 71 to 100 percent and 82 to 100 percent, respectively. These rates will vary as the
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population is reaching carrying capacity or exceeding carrying capacity. Cow moose will
typically have one to two calves per year but the rate at which twinning occurs is correlated
with the habitat quality and the carrying capacity of the population, triples have been observed
in wild moose populations but is rare. Barren cows are termed as an individual that do not go
into heat and therefore, will not be bred, while this is rare it does occurs (Franzmann and
Schwartz 1998).
After successful birth of a calf or calves, a new cycle starts, which consists of lactation,
weaning, dispersal, and breeding. Once the calf(s) have been born, the cow will clean her
calf(s), eat the placenta, lick up any amniotic fluid, eat soil, and the calf(s) will being to nurse.
The cleaning of the calf is the initial bonding between the cow-calf pair. This cow-calf bond is
an important part in the survival of the calf. This bond takes 7-8 days to develop and is due to a
constant contact between the cow and her calf. The calf is relatively immobile for a few days
up to the first week of life so the cow will be tied to the site until the calf is strong enough to
travel. For the first week the cow stays within visual or vocal communication with the calf,
typically within a 50 meters radius to the calf (Franzmann and Schwartz 1998).
Predation
Predation on neonate ungulates in most populations does not appear to have a
significant effect on population growth because survival of older individuals has a greater effect
on population size than neonate survival. However, in the case of moose neonate survival is a
limiting factor that decreases population growth (Patterson et al 2013). There are a few
different large mammalian predators that are attributed to preying on moose and moose calves
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throughout their range. These apex predators include wolves (Canis lupus), American black
bear (Ursus americanus), brown, and grizzly bears (Ursus arctos). While some literature
suggests the eastern coyote (Canis latrans) will predate upon moose calves, this is considered
to be a rare event. Edward (1983) suggested that predator's effects prey by preying on them
and altering prey habitat selection due to predator avoidance. When there are multiple
predators on the landscape each can have their own effect on moose and calf survival and
habitat selection. Ballard (1992) suggests that predation rates by each predator are density
dependent. This is based on the density of each predator in that area and moose densities.
When moose are at or near vegetative carrying capacity calf mortality is compensatory; this
typically occurs when moose are at high densities. When moose are at low densities Ballard
(1992) suggest that predation is additive .
Wolf Predation
Wolves are species specific predators, typically predating on ungulate species. Wolf
predation has been shown to be a limiting factor of population growth for many ungulate
populations. Wolves account for 3-9% of calf mortality (Ballard 1992). Ballard (1992) suggests
that wolves are the main predator of adult moose during the winter months when moose
mobility is low due to high snow depth. This allows for wolves to be a lot more successful
hunters than during the summer season when moose are highly mobile and able to defend
themselves .
Bear Predation
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Bears have been shown to be a significant source of calf mortality and contribute to
both additive and compensatory mortality. When moose densities are low then bear predation
on moose calves creates additive mortality (Ballard 1992). When the bears are the only
predator on the landscape predating moose calves account for 30-70% of calf mortality (Ballard
1992 and Garneau et al 2007). When other apex predators inhabit the same area as black
bears, they do not have as high of predation rates. Some studies suggest that when black bears
and grizzlies are both on the landscape they can be a significant sources of mortality and ,
become an additive source of mortality of calves (Ballard 1992). A study in Saskatchewan found
when moose densities were low mortality of moose calves was additive. When bear numbers
were reduced there was an increase in calf survival that year. Immediately following the
stopping of bear reductions calf survival rates returned to their orrginal levels prior to bear
removal (Ballard 1992).
The main predator of moose calves throughout the eastern part of their range is the
American black bear (Franzmann and Schwartz 1998 and Ballard 1992). Multiple studies
conducted have determine that bear predation can be a limiting factor on moose population
growth. Black bears are generalist omnivores that predate on both plant and animal matter.
They alter their forage selection based on what is available for them to eat on the landscape.
Following den emergence black bears primary consume highly digestible plant and animal
matter (Bastille-Rousseau et al 2010). Black bears typically forage on ·high quality vegetation
that typically emerges sooner in lowland areas therefore bear-moose interactions are higher
during this time period. Bastille-Rousseau (2010) suggested that roadsides and wetlands
offered the most amount of vegetation for bears to forage on earlier in the season, and shrub
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lands, regenerated clear-cuts, and mature conifer stands offered the lowest amount of forage
available so bears typically avoided using these areas. Bears moved from one patch of high
quantity vegetation to the next and spend shorter amounts of time foraging in these high
quantity areas. Bastille-Rousseau et al (2010) and Garnueau et al (2007) found that bears are
not actively searching for neonates and that most encounters with ungulate neonates occur
during inter-patch movements. Therefore supporting that bears are opportunistically feeding
on moose and actively searching for neonates. The habitats were bears are· foraging the most
on high quantities of forage is going to increase their probability of encountering moose that
are also selecting for areas of high forage availability (Bastille-Rousseau et al 2010 and Garneau
et al 2007).
Predation Adaptations
Neonate ungulates are most vulnerable to predation during the first month of life
(Bastille-Rousseau 2010). Therefore, most ungulate neonate species have special adaptations
for predator avoidance and hiding. While cow moose will defend their offspring, calves do not
have any adaptations for hiding from predators. Calves are termed as "laying-out", while on
the calving ground. The calf lays on its sternum with forelimbs out in front of its body and head
laid between front legs (Franzmann and Schwartz 1998).
Once the cow-calf pair leaves the calving ground, the chance of predation typically
increases due to the movement of the cow-calf pair into new areas that could be potentially
occupied by predators (Franzmann and Schwartz 1998) .
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Moose have been observed to alter their diets based on the predators in the area and
the habitat that predators typically search for food. Cow moose will move into areas for calving
purposes that are low in forage quality and quantity in the attempt to mitigate predation ·
probability. Cows in Isle Royale Wilderness National Park, Michigan were observed moving to
lake shorelines of peninsulas and islands to calve. This is interpreted to be an adaptation to
avoid predators which leads them to select for area of low forage quality and quantity (Edwards
1983).
Calving Site Classification
While moose calving selection throughout their range is being studied, only two papers
in the literature discuss calving characteristics in northeastern North America, Leptich and
Gilbert (1986) and Scarpitti et al. (2007). Prior research indicates that calving site across their
range have been characterized as secluded areas, that minimize the chance of predation, have
high quality forbs to meet the high energy demand of lactation, and are near water resources.
In Maine calving site parameters that were influential in distinguishing calving from
random sites consisted of stand density, tree size, drainage, and accompanying upland or
lowland vegetation, stand closure, and site disturbance (Leptich and Gilbert, 1986). Calving
sites have been characterized by "undisturbed and poorly drained sites often dominated by
cedar although non-forested calving sites area also represented. Typically close to water and
may have small diameter browse species present on the site." (Leptich and Gilbert, 1986).
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In New Hampshire Scarpitti et al (2007) found no significant difference between random
and calving sites in most variables they investigated but there were a few parameters that did
differ. Distance to cut/regenerated patches was twice as close for random sites compared to
calving sites. Calving locations were also 100 meters farther from roads than random sites but
this difference was not statistically significantly. There was also no difference in elevation of
calving or random sites (Scarpitti et al. 2007).
In Minnesota calving sites were characterized by (McGraw et al. 2014) forest diversity
and were typically located in bogs. The use of bogs was higher than the availability of the bog
cover and allowed calves to have high hiding cover, greater forage, and access to water. As the
cow calf pair move more the use of mixed and young and regenerating forests increased
(McGraw et al. 2007).
In Alaska calving sites were distinguished by 3 three parameters: forage, aspect, and
visibility (Bowyer et al,. 1999). The amount of forage was significantly greater at birth sites than
random sites, this was attributed to the amount of willow at the birth sites. Birth sites were
also 96 meters higher than random sites, which would allow the cow to have greater visibility at
the calving site. The majority of calving sites were located on southeastern exposures which
can be attributed to green up occurring earlier in spring on aspects in that direction. However,
cows did not calve closer to human developments compared to randomly selected sites
(Bowyer et al. 2007).
Objectives
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The objective of this study are (1) identify different habitat types selected by cow moose
as calving sites; (2) identify land use practices that influence cow moose calving habitat; (3)
inform managers on habitats used by cow moose as birth sites and during immediate post­parturition;
(4) compare results from other studies looking at cow moose birth site selection,
particularly in northeastern North America
Hypotheses
Cow moose will select calving locations that will decrease the probability of predator
interaction but will still offer attainable browse to meet the high energetic demands of
lactation. I predict that cow moose will therefore select for sites that offer greater
concealment cover for their neonates than randomly selected sites. Increase in concealment
cover will provide the calf with more hiding cover. This is going to decrease the probability of
predators detecting the calf. Calving sites will be characterized by being closer to water
resources and farther from roads than randomly selected sites. Increasing the distance from
road is going to decrease the probability of predator interactions that use roads as travel
corridors. A decrease in the distance from water resources is going to allow for water to be
available when on the calving ground. Large water bodies can also be escape routes for the
cow-calf pair to evade predators. Finally, cows are going to select for areas that are well
drained and have a higher diversity of habitat types than randomly selected sites. Well drained
locations are going to allow the calf to lay on the calving ground and not be wet. This is going
to help decrease the probability of the calf getting sick. Selection for areas that have a higher
diversity of habitat types is going to increase the amount of forage available to cows. This
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increase is going to allow cows to have more forage for a longer time when on the calving
grounds as oppose to homogeneous landscapes.
METHODS
Study Area
All study animals were located in Somerset County, ME -except for one cow whose
home range was half in Piscataquis County. The study area consists of both public and private
lands in Northwestern Maine (Figure 1). The cow moose home ranges were established in the
Western Moosehead region over to Jackman, and as far South as The Forks, and east to Shirley
Mills. This area in Maine is heavily logged for commercial products; therefore, the landscape is
continually changing. The largest logging company in the area is Plum Creek; they own 861,000
acres throughout Maine with 363,000 acres in the Moosehead Region (Plumcreek.com).
Home Range Analysis
The Maine Department of Inland Fisheries and Wildlife (MDIFW) deployed Vectronics
Vertex Survey GPS collars on adult and juvenile moose in two study areas {Somerset county and
northern Aroostook county). MDIFW started deploying these collars in 2014 and have 149
moose collared between these two study areas. The GPS collars are programed to take
locations on a twelve hour interval at 9:00 and 21:00. These collars also collect activity data
that measures the x, y, and z movement of the individual. Home range~ were established using
a Brownian bridge movement model (BBMM) (Horne et al 2007 and Kranstauber et al 2012).
The BBMM allows for greater accuracy when estimating home ranges and core areas because it
takes into account the distance moved between observations, activity during that time period,
and the error associated with the observation {Horne et. al., 2007).
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I developed home range estimates for 14 adult cows that were possibly pregnant.
Home range analysis was done in program R using the adehabitat package. Missed GPS fixes
were recorded as zeros to maintain a constant time interval between points. I selected a time
frame from the 25 April till the 25 June. This allowed for the home ranges to be created that
explicitly showed the time period leading up to parturition and post parturition. Next, I used a
trajectory graph to assess the distance movements of moose between consecutive locations.
The trajectory of a parturient moose is characterized by a long distance movement then a
reduction in movement for approximately a week (McGraw et. al., 2014 and Severud et. al.,
2015}. Next, I created a utilization distribution to locate the areas of highest probability for that
individual. Using the BBMM I created a 90% and 20% core areas for each individual. Finally, I
imported the core areas and associated points into ARCmap for further analysis.
Once all of the shapefiles were loaded into ARCmap the points for each moose and the
20% kernels were further examined. Using the trajectory projections, GPS points, and 20% core
area I located the specific kernel that was used for calving. This helped to visually see the
movements of each individual moose before entering the 20% kernel and once in the kernel.
After determining the 20% kernel used by the moose two random points were assigned to both
that 20% kernel and the 90% kernel.
Site selection
When determine sample locations I assigning head and tails to a set of coordinates for
the calving sites and random sites. Then I flipped a coin to determine which site was sample.
Field Data Collection
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The same data were collected at both sites (birth and random) to allow for comparative
analysis of the composition of each site. I collected was both physical and vegetative
measurements. The data collected at each site consisted of percent canopy cover;
concealment cover determined using a ball-staff method; total basal area (BA) with a basal area
factor (BAF) of 10; measurement of the diameter at breast height (DBH) and record of tree
species; the number of snags (DBH >Scm) within a 10 meter radius of plot center; soil sample;
records of dominant canopy and tree species; and dominant habitat type around plot center.
The dominant habitat type was visually assessed at each site and a predesignated
habitat classification was selected (Table 1). The dominant tree species and canopy species
were visually assessed at each site. The canopy species was determined by the amount of leaf
cover in the upper level of the canopy. This excluded younger and lower height trees from
being considered as canopy species.
The percent canopy cover was then determined using a concave spherical densiometer
10 meters from plot center in all four cardinal directions (north, south, east, and west) (Field
Procedures 2009). The percentages of each direction were averaged to obtain a single percent
canopy cover for that site. Next, the concealment cover was estimated using a ball-staff
method (Collins and Becker 2001). Two balls (10 centimeter in diameter) were placed on a 1.5
meter rod; one ball was positioned at 0.5 meter and the other at 1.0 meters above the ground,
and both were viewed at 10 and 25 meters in all four cardinal directions. Then the visibility of
the line where the ball and rod meet was determined (Collins and Backer 2001).
Basal area was determined using a Cruz-all prism with a BAF of ten measured from plot
center (Robertson 2000). The trees that were counted in then had the DBH and species of the
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tree recorded. The number of snags (DBH>Scm) were visually counted within a ten meter
radius of plot center (Robertson 2000).
Soil samples were collected at 10 meters from plot center in all four cardinal directions
if soils could be extracted. First, the area around the site was cleared of leaf litter. Then, the
soil core was drove 8 centimeters into the substrate. Once the correct depth was reached the
area around the core was excavated so a piece of metal could be inserted at the bottom of the
core to ensure no soil was lost upon extraction. The soil was then put into a Whir-pack and
labeled with corresponding site information such as moose id, site classification, date, and
direction from plot center. The soil weights were immediately recorded and the volume of the
soil core was also recorded.
Soil Lab Processing
Once all of the soil samples were collected from in the field, they were processed in the
Unity College Geoscience Lab. Soil processing was conducted to estimate the average soil
porosity per site. The formula used for calculating porosity is as follows Pdry = Pp(l-n), where
Pdry is dry bulk density (Pdry =(Ms/Vt), Pp is the density of soil particle, and n is porosity (Ward
and Trimble 2004). First, beakers were labeled with site information and weighted empty.
Then, soil was transferred from the Whir-pack to a labeled beaker and weighed again. Next,
the beakers were placed in a furnace at a temperature of 105 degrees Celsius for 24 hours. The
drying period removed all moisture from the soil, and the beakers of soil were reweighted. This
allowed for the dry bulk density to be calculated using the dry weight and volume from the field
core. Each individual soil sample was then placed into a lOOOml beaker with 350ml of water .
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The final volume of water was recorded after the soil was added. Finally, after rearranging the
equation for dry bulk density, porosity values were calculated (Ward and Trimble 2004).
Spatial Data Collection
After completion of all the field data collection, the points sampled in the field were
then put into ARCmap. Once in ARCmap, the elevation, slope, and aspect were derived from a
digital elevation model. The distance to nearest road, stream, and open body of water was
calculated using the near tool in the Arctoolbox. The percent of each vegetation type (Macro
layer for Maine TNC) at each site was determined with a one hundred meter buffer around
both calving and random site.
Elevation, slope, and aspect were all derived from a digital elevation model for Somerset
County from the Maine GIS database (Maine Office of GIS). The distance to nearest feature tool
in ARC tool box was used to calculate the distance to nearest road, stream, and open body of
water from each sampled site.
The use of a Shannon-Weiner index was used to calculate the species richness of each
site within a 100 meter radius of each sampled site (Silvy 2012). Using the buffered calving and
random sites I extracted the different habitat types. Then, using the area of each habitat type
within the buffer I calculated Shannon-Weiner index value. This allowed for habitat diversity to
be determined between calving and random sites.
Data Analysis
The data for this study was analyze9 using R studio to develop and run models to test
the relationships between site parameters and the likelihood of it being either a birth site or a
random site. This study used generalized linear models (glm) to determine the relationship
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between site classification and parameters collected at each site. Spatial and in-field collected
data were analyzed separately because of differences in sample size (spatial n=14 and in-field
n=lO). Spatial data models were run, and top performing models helped to inform model
construction for in-field measured parameters. Along with the models constructed a null model
was run to determine the predicting power of all parameters into one model.
Model Selection
Model comparison was done by evaluating AICc values for each model constructed. The
lower the Al Cc value, the better fit the model had to the data. The top performing parameters
(those that did not encompass 0 in their confidence intervals) were rerun using an interaction
model with another high performing parameter. The use of interaction models allowed for a
more complex analysis of multiple parameters to predict site classification in a nonparallel
distinction.
RESULTS
Sampling of both spatial and In-field data took place during the fall of the 2015. There
were 14 parturient moose sampled spatially and of those 10 were sampled In-field. Each birth
site sampled had an associated random site sampled for each moose.
All of the models that were considered further had to be within 2 AIC of the top
preforming model. Any model that was within 2 AIC showed that it fit the data and could have
explanatory power when distinguishing calving locations from random locations. The spatial
model results are in Table 2 and the In-field model results are in Table 3. The spatial parameter
that had the most explanatory power for the probability of predicting a birth site was distance
from road. This had a positive relationship whereas the distance from road increases the
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probability of a calving site also increases. The next top model was distance to stream. This
model had a positive relationship whereas the distance from stream increases the probability of
a calving site occurring also increased. When slope was tested it was the next best performing
model. There was a negative relationship shown, when slope decreased and became more flat
the probability of it being a calving site increased. The next model was an interaction effect
between distance to stream and slope. This model showed that as distance from stream and
slope increase the probability of a calving site occurring decreases. Natural drainage class was
then tested resulting in a positive relationship. As the soil class increase (better drained) the
probability of a calving site occurring will also increase. An additive model was constructed
testing the distance to road and distance to stream. This model showed that as the distance
from both road and stream river increases the probability of a calving site occurring also
increases. The final model that is within 2 AIC was an interaction effect between distance from
road and slope. This model showed that as the distance from road increases and the slope
decreases the probability of a calving site occurring will increase. Therefore, the parameters
that are most influential in determining calving sites are distance to road, distance to stream,
slope, and natural drainage class.
The In-field model testing had one model that outperformed all other models in this
data set. This model showed that as obstruction at a height of 0.5 meter at a distance of 10
meters from plot center increase the probability of calving will also increase. All other models
were not within 2 AIC of this model. However, the top six models tested are presented in Table
3.
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In the spatially explicit data there was no support for predicting calving sites using
elevation, aspect, habitat classification, Shannon-Weiner Index, and distance to water bodies.
However, most of the sampled locations fell in boreal upland forests, northern hardwood
forests, and northern swamps. The percent canopy cover, total basal area, species specific
basal area, and total number of snags showed no correlation to predicting calving site selection.
DISCUSSION
The results show that calving sites in Northern Maine are characterized by having high
obstruction levels around the birth site at 0.5 meters above the ground. They are also
characterized as being located far from roads and streams, on gentle slopes, and typically on
well drained soils.
The use of areas by parturient moose that are characterized as having high concealment
cover at a 0.5 meter height can be seen as a way to make the calf or calves harder to detect by
predators. Therefore, it would increase the calf's survivorship while on the calving grounds in
the chance of having an encounter with a predator. The importance of concealment cover
when on the calving grounds suggests that cows are expressing a predator avoidance
mechanism. This study was unable to directly measure if forage availability (due to time of
sampling) was a possible driving force in calving site selection. This could have been an artifact
of the high obstruction levels around the birth site compared to random sites. These results
support what Langley and Pletscher (1994) found that concealment cover from 0-1 meter to be
an important characteristic of calving sites in Montana and Southeastern British Columbia.
However, Bowyer et al. (1999), Leptich and Gilbert (1986), and Scarpitti et al. (2007) found that
28
were was not a significant difference in concealment cover between birth sites and random
sites in Alaska, Northern Maine, and New Hampshire, respectively. The data collected support
the first prediction that calving sites are characterized as having higher concealment cover than
randomly selected sites.
Calving site were also more likely to occur as the distance from streams increased, but
the distance from water bodies was not an explanatory variable in calving site selection.
Distance from flowing water can be explained as a predator avoidance mechanism because
riparian habitats have been shown as predator foraging corridors. This is due to the amount
and diversity of vegetation emerging in these areas that are available for bears to forage on
after emerging from denning (Bastille-Rousseau et al. 2010).
My result found that there was not relationship between the distance to water body
and calving site selection therefor supporting Scarpitti et al. (2007) and Leptich and Gilbert
(1986). However, the results support that distance from streams is an influential characteristic
in calving habitat selection. This does not support my prediction that calving site selection will
be closer to water bodies because there was not a difference in distance to water bodies
between calving and random locations. The density of water bodies in each study area could
have been influencing the difference in distance from both calving and random sites.
The results suggest that calving sites were characterized as being farther from roads
than randomly selected sites. These results support the prediction that calving sites will be
father from roads than randomly selected sites. However, Leptich and Gilbert (1986) and
Scarpitti et al. (2007) found no difference in the distance from roads to birth or random sites.
29
This could be a predator avoidance behavior because they associate these features with travel
or hunting corridors for black bears (Bastille-Rousseau et al. 2010). The difference in the
distance from roads can be a function of the amount and density of roads in each study area
and the amount of newly developing roads in area with heavy logging. Bastille-Rousseau et al.
2010, found that along paved and unpaved roads there was a greater amount of vegetation
abundance that black bears foraged along.
Slope was found to have predicting power between birth and random sites. Cow
selected for areas that had gentler slopes than randomly selected sites. Therefore it supports
the prediction made that cows will select for areas with decreased slope than randomly
selected sites. These findings contrasts with Gilbert and Leptich (1986) and Scarpitti et al.
(2007) who found no significant difference between slope at birth and random sites. This could
be due to the more mountainous region of this study compared to previous studies. A decrease
in the amount of slope would aid the neonate in mobility around the calving site therefore
allowing it to better evade predators if they came into contact.
The natural drainage class had some predicting power suggesting that the better
drained soil class the high probability of calving. These results support the prediction made
that cows are going to select for areas with well drained soils compared to randomly selected
sites. This is contrary to what Leptich and Gilbert (1986) found that suggests calving sites were
classified as being poorly drained sites. Sites that are not will drained would have a higher
probability of containing aquatic vegetation and browse that would emerge sooner on spring as
compared to dryer sites. Bears will typically forage on newly emerging vegetation therefore
30
feeding in sites that would be wet during late spring. Bears will typically use wetter locations as
travel corridors because it allows them to cool off their pads to help regulate body
temperature.
This study was unable to detect any major vegetation classifications that are selected by
moose for calving. This could a product of the heterogeneous landscape where this study was
conducted. Land use practices such as logging also help to increase the amount of
heterogeneity on the landscape. Therefore, the prediction that cows are going to select for
calving sites that have high forest heterogeneity was not supported by the results.
CONCLUSION
The results of this study show that moose are selecting for physical and vegetative
parameters that are going to mitigate the probability of predator-moose interactions when on
the calving grounds. The parameters that they selected for are high concealment cover,
increased distance from both roads and streams, gentler slopes, and well drained soils.
Study Improvements
There are a few areas where this study could be improved. The first improvement
would be to have better estimations of calving locations. This could be accomplished my
conducting walk-in surveys to observe cow/calf pairs at the birth site. Deploying vaginal
implant transmitters (VIT) into the birth cannel of pregnant moose would allow for the most
accurate estimation of were parturition took place. However, this would add in extra expenses
for purchasing the VITs and monitoring them till parturition occurs. A cheaper solution to this
31
problem would be to increase the amount of fixes that are taken by the collar around the time
of parturition to be able to identify large movements sooner and see localizations more
accurately. This would also allow for more chances for fixes to be taken. At the current 12
hour interval if a single fix is missed the individual is not accounted for a 24 hour period.
Another improvement would be conducting sampling of all points after the cow-calf pair
leaves the calving grounds. This would allow for more accurate measures of concealment cover
and canopy cover at both sites. Sampling after the pair leave would allow for identification of
important browse species and allow for sampling of available forage at the birth site.
The use of a different way to extract the soil cores would have allowed for greater
accuracy when classifying soils types at each site. The core that was used did not allow for easy
extraction from the core into the sample bags. This could have led to the loss of soil and
therefore inaccuracy in the sampling method. The diameter of the core also made it more
difficult to extract soil at some sites due to the amount of rock and/or root obstructions which
led to no soil being able to be extracted in some situations. The use of a soil probe would have
allowed for soil extraction to be more accurate and easiertransportation to the sample bags
with less loss of soil. This would have also allowed for fewer roots to be in each sample making
the displacement values more accurate.
Future work
If this study was to be conducted again I would suggest making all of the changes in the
above section to allow for more accurate data to be collected. I would also suggest trying to
32
collect an index of forage at each site to better address if the cow was selection for forage
availability or predator avoidance. I would collect all of the same spatial data with the addition
of trying to address calculating the distance to hard edges and logging sites. Estimating the
distance to logging also trying to determine the age of the cut and if there is any relationship to
cut age and probability of use.
Increasing the sample size will help to support the conclusions drawn. Including moose
over consecutive years to assess whether or not cows are selection for the same physical and
vegetative features form one year to the next.
Management Implications
This research can be used to inform managers and foresters on how land use changes
can effect calving habitat selection. Calving site selection is not random therefore the cow is
selecting for features that are both physical and vegetative to increase the probability of
survival while the cow-calf pair is on the calving grounds. If calving habitat is limited this could
lead to a decrease in the survival of calves and an overall decrease of adult moose being
recruited into the population. Calf survival is in most areas a limiting factor to population
growth and therefore could be what is keeping the moose population below its vegetative
carrying capacity. If there is an increase in the amount of preferred calving habitat this could
increase calf survival and therefore increase moose population size.
The use of BBMM in determining calving sites could be implemented to save on costs of
walking in on cow-calf pairs and risking maternal abandonment. This could also eliminate the
33
use of VITs in certain cases and decrease the cost of buying, deploying, and continually
monitoring VITs frequencies.
34
Figures/ Tables
Figure 1. Map of the birth and random sites sampled.
35
Habitat Types:
Table 1. The habitat type in field classification scheme with associated acronyms.
Deciduous upland dcup
Deciduous lowland dell
Coniferous upland cf up
Coniferous lowland cfll
Mixed upland mxup
Mixed lowland mxll
Cut-regeneration ctrg
Other other
36
II
II ..
II
Ill
II
Ill
Ill
Table 2 Spatial data Models
Model Spatial 0-1 (Random-
Testing Data Birth)
Model Model Y-Response_Var Variable(s) AIC Confidence
Rank Code (Variable)
1 msl Site Dis_Rd 40.66 (-0.0003 I 0.0022)
2 ms7 Site Dis_SR 41.47 (-0.0009 I 0.0038)
3 msll Site Slope 41.76 (-0.4129 I 0.0833)
4 ms15 Site Dis_SR * Slope 42.10 (-0.0001I0.0095)
5 ms2 Site Natural.drainage.Class* 42.13 (-0.3114 I 0.8194)
6 ms8 Site Dis_SR + Dis_Rd 42.24 (-0.0017 I 0.0035)
7 ms14 Site Dis_Rd. * Slope 42.56 (-0.0011, 0.0031)
*Natural drainage class is very poorly drained to well drained {1-6}
msl: As the distance to road increases the probability of a calving site is going to increase.
ms7: As the distance to SR increase the probability of a calving site is going to increase.
msll: As slope increase the probability of a calving site is going to decrease.
Confidence
(Variable)
(-0.4633 I
0.5830)
(-0.0005 I
0.0022)
(-0.9020 I
0.3231)
mslS: As distance to SR increase and slope increase the probability of a calving site is going to decrease.
ms2: As soils become more well drained the probability of a calving location is also going to increase.
ms8: As both distance to rd and SR increase the probability of a calving location will also increase.
Confidence
(Variable)
(-0.0014 I 0.0003)
(-0.0003 I 0.0005)
ms14: As distance to rd increase and slope decreases there is an increase in the probability of a calving location occurring.
37
Table 3 In-field data Models
Model ln-field_Data 0-1 (Random-Birth)
Testing
Model Rank Model Code Y-Response_ Var Variable(s) AIC Confidence Confidence Confidence
(Intercept) (Variable) (Variable)
1 mfll Site Ball_1_10M 24.58 (1.0584 I 10.9281) (-14.1168 J
-1.5653)
3 mf13 Site Ball_1_25M 27.55 (-0.3637 I 2.2543) (-8.7128 I
0.1380)
4 mf14 Site Ball_1_10M 28.48 (-0.5431I15.6905) (-20.9618 J (-22.2648 J
*Ball 2 lOM 0.8676) 24.1661)
5 mf15 Site SM 29.7 (-0.6741 I 1.3532) (-0.1231 I
0.0126)
6 mf12 Site Ball_2_10M 30.88 (-0.8924 I 2.0576) (-5.2804 I
1.9153)
Ball-Staff readings: Ba/11 (O.Sm) Ba/12 {1 .0m) line observed no (O) yes (1)
mfll: At a distance of 10m as site obstruction increase at a height of 0.5m the probability of it being a calving site is going to
increase.
mf8: These are highly correlated variable so they are very explanatory when all combined.
mf13: At a distance of 25m as site obstruction increase at a height of 0.5m the probability of it being a calving site is going to
increase.
Confidence
(Variable)
(-26.3945 I
27.8144)
mf14: At 10m as obstruction increases at O.Sm of height but decrease at 1.0m of height the probability of it being a calving site will
increase
mflS: As sugar maple DBH decreases probability of calving location increases
mf12: As obstructions decrease at lm of height at a distance of 10m the probability of calving will increase.
38
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